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Strong employer brand and metro location but specialized GenAI/agentic skillset limits applicant density.
Applied AI and agentic engineering skills are transferable across industries but require strong ML-specific expertise.
Explicit years plus mandatory GenAI, cloud-native, and ML tech stack create stringent shortlisting filters.
Build and enhance full-stack products with embedded GenAI and agentic AI capabilities focused on high-quality, outcome-driven solutions.
Own all phases of product engineering lifecycle including requirement analysis, design, development, testing, integrations, deployment, and maintenance, ensuring code integrity and alignment with business goals.
Implement cost-aware, lean, and automated engineering practices from discovery to production using modern AI/ML and cloud-native tools across cross-functional teams.
Bachelor’s degree in computer science, software engineering, data science, machine learning, or related discipline.
5+ years of experience with technologies like Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph.
3+ years of hands-on experience in AI/ML and agentic applications, specifically GenAI and LLM integration with tools like OpenAI or Anthropic.
3+ years of cloud-native engineering experience on Azure, AWS, or GCP including AI/ML cloud services, infrastructure-as-code, and cost-aware engineering (FinOps).
Experienced full-stack engineer with deep applied AI knowledge, especially in building GenAI-powered, agent-enabled products end to end with measurable business impact.
Comfortable working in lean, agile, DevSecOps-driven environments, utilizing cutting-edge tools and frameworks to deliver continuously and maintain high code quality.
Capable of collaborating across cross-functional teams to translate complex business and user needs into scalable technical solutions and drive consensus on product goals.